Although language modeling has been trending upwards steadily, models available for low-resourced languages are limited to large multilingual such as mBERT and XLM-RoBERTa, which come with significant overheads deployment vis-à-vis their model size, inference speeds, etc. We attempt tackle this problem by proposing a novel methodology apply knowledge distillation techniques filter language-spec...